Field of the Invention
The present invention relates generally to predicting toxicity of compounds or agents
Description of the Prior Art
The societally much needed ability to predict human toxicity of chemical compounds would be helped by defining the complete, or core, set of genomic changes that could causally explain why the dose generally makes a chemical into a poison at the cellular and organismal levels. However chemical risk assessment currently proceeds without taking into account this information. A clear problem in the interpretation of the mass of data generated by genome-wide profiling technologies is both that some results cannot be mechanistically interpreted, but also that too many potential inferences can be made and the inferences therefore are not sufficiently specific or predictive.
In 2012, the Organization for Economic Co-operation and Development (OECD) launched a new program on the development of Adverse Outcome Pathways. An Adverse Outcome Pathway (AOP) is an analytical construct that describes a sequential chain of causally linked events at different levels of biological organization that lead to an adverse health or ecotoxicological effect. AOPs are the central element of a toxicological knowledge framework being built to support chemical risk assessment based on mechanistic reasoning. AOPs include chemical mode-of-action (MoA) and toxicity pathway activities that can be inferred from chemical perturbations gene expression data if it is known which gene activities contribute to them. Furthermore toxicity is predicted from the Thresholds of Toxicological Concern (TTC) concept, namely establishment of a generic exposure level for (groups of) chemicals below which there is expected to be no appreciable risk to human health. The current concept of TTC is relatively loose and relies on structure-based read-across, as well as on structural alerts for toxicity, including carcinogenicity.
Availability of large human omic's data sets (a general term for a broad discipline of science and engineering for analyzing the interactions of biological information objects in various 'omes such as genome or metabolome) enables data-driven modeling and model-driven data analysis of toxicity leading to Big Data analytics (a collection of data sets so large and complex that it becomes difficult to process using traditional database management).
There is a need for an efficient tool that can be used to predict the toxicity of an agent, such as within the chemicals, pharmaceuticals, cosmetics or agrochemicals industries.